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@article{DBLP:journals/corr/abs-1203-3099,
  author    = {Otman Abdoun and
               Jaafar Abouchabaka and
               Chakir Tajani},
  title     = {Analyzing the Performance of Mutation Operators to Solve the Travelling
               Salesman Problem},
  journal   = {CoRR},
  volume    = {abs/1203.3099},
  year      = {2012},
  url       = {http://arxiv.org/abs/1203.3099},
  archivePrefix = {arXiv},
  eprint    = {1203.3099},
  timestamp = {Mon, 13 Aug 2018 16:48:44 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1203-3099},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1203-5028,
  author    = {Otman Abdoun and
               Chakir Tajani and
               Jaafar Abouchabaka},
  title     = {Hybridizing {PSM} and {RSM} Operator for Solving NP-Complete Problems:
               Application to Travelling Salesman Problem},
  journal   = {CoRR},
  volume    = {abs/1203.5028},
  year      = {2012},
  url       = {http://arxiv.org/abs/1203.5028},
  archivePrefix = {arXiv},
  eprint    = {1203.5028},
  timestamp = {Mon, 13 Aug 2018 16:48:40 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1203-5028},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@Book{Ackley:1987:CMG:40713,
  Title                    = {A Connectionist Machine for Genetic Hillclimbing},
  Author                   = {David H. Ackley},
  Publisher                = {Kluwer Academic Publishers},
  Year                     = {1987},

  Address                  = {Norwell, MA, USA},

  ISBN                     = {0-89838-236-X}
}

@Book{back1996evolutionary,
  Title                    = {Evolutionary Algorithms in Theory and Practice},
  Author                   = {Thomas Back},
  Publisher                = {Oxford Univiversity Press},
  Year                     = {1996},

  Timestamp                = {2012.02.11}
}

@Article{baker1987selection,
  author    = {James E. Baker},
  title     = {Reducing Bias and Inefficiency in the Selection Algorithm},
  journal   = {Proceedings of the Second International Conference on Genetic Algorithms and their Application},
  year      = {1987},
  pages     = {14--21},
  timestamp = {2012.02.11},
}

@InProceedings{Baluja95removingthe,
  Title                    = {Removing the Genetics from the Standard Genetic Algorithm},
  Author                   = {Shumeet Baluja and Rich Caruana},
  Year                     = {1995},
  Pages                    = {38--46},
  Publisher                = {Morgan Kaufmann Publishers}
}

@InCollection{Bandaru2016,
  author    = {Bandaru, Sunith and Deb, Kalyanmoy},
  title     = {Metaheuristic Techniques},
  booktitle = {Decision Sciences},
  publisher = {CRC Press},
  year      = {2016},
  pages     = {693--750},
  month     = nov,
  comment   = {doi:10.1201/9781315183176-12},
  doi       = {10.1201/9781315183176-12},
  issn      = {978-1-4665-6430-5},
  timestamp = {2016.12.06},
}

@Article{Tina2011,
  author  = {Heiko Bauke},
  title   = {Tina's Random Number Generator Library},
  journal = {https://github.com/rabauke/trng4/blob/master/doc/trng.pdf},
  year    = {2011},
  url     = {https://www.numbercrunch.de/trng/trng.pdf},
}

@Article{Blickle97acomparison,
  Title                    = {A Comparison of Selection Schemes used in Evolutionary Algorithms},
  Author                   = {Tobias Blickle and Lothar Thiele},
  Journal                  = {Evolutionary Computation},
  Year                     = {1997},
  Pages                    = {361--394},
  Volume                   = {4}
}

@Book{chawdhry1998soft,
  Title                    = {Soft Computing in Engineering Design and Manufacturing},
  Author                   = {Chawdhry, P.K. and Roy, R. and Pant, R.K.},
  Publisher                = {Springer London},
  Year                     = {1998},

  Doi                      = {10.1007/978-1-4471-0427-8},
  ISBN                     = {9783540762140},
  Lccn                     = {97031959},
  Url                      = {https://books.google.at/books?id=mxcP1mSjOlsC}
}

@Book{dawkins1986,
  Title                    = {The Blind Watchmaker},
  Author                   = {Dawkins, Richard},
  Publisher                = {New York: W. W. Norton \& Company},
  Year                     = {1986},

  ISBN                     = {0-393-31570-3},
  Url                      = {https://books.google.at/books?id=-EDHRX3YYwgC}
}

@Book{Luke2013Metaheuristics,
  title     = {Essentials of Metaheuristics},
  publisher = {Lulu},
  year      = {2013},
  author    = {Sean Luke},
  edition   = {second},
  note      = {Available for free at http://cs.gmu.edu/$\sim$sean/book/metaheuristics/},
  abstract  = {This is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts. It was developed as a series of lecture notes for an undergraduate course I taught at GMU. The chapters are designed to be printable separately if necessary. As it's lecture notes, the topics are short and light on examples and theory. It's best when complementing other texts. With time, I might remedy this.},
}

@Book{michalewicz1996genetic,
  Title                    = {Genetic Algorithms + Data Structures = Evolution},
  Author                   = {Zbigniew Michalewicz},
  Publisher                = {Springer},
  Year                     = {1996},

  ISBN                     = {9783540606765},
  Lccn                     = {95048027},
  Url                      = {http://books.google.at/books?id=vlhLAobsK68C}
}

@Book{Mitchell1998,
  Title                    = {An Introduction to Genetic Algorithms},
  Author                   = {Melanie Mitchell},
  Publisher                = {MIT Press},
  Year                     = {1998},

  Address                  = {Cambridge, MA, USA},

  ISBN                     = {0262631857}
}

@Book{Nijenhuis1978,
  Title                    = {Combinatorial Algorithms for Computers and Calculators},
  Author                   = {Albert Nijenhuis and Herbert Wilf},
  Publisher                = {Academic Press},
  Year                     = {1978},
  Edition                  = {Second},

  ISBN                     = {0-12-519260-6}
}

@Article{OracleJDKValueBasedClasses,
  Title                    = {Value-based classes},
  Author                   = {Oracle},
  Journal                  = {https://docs.oracle.com/javase/8/docs/api/-java/lang/doc-files/ValueBased.html},
  Year                     = {2014},

  Timestamp                = {2014.11.12}
}

@Article{Palmer1995,
  author    = {Palmer, Charles C. and Kershenbaum, Aaron},
  title     = {An approach to a problem in network design using genetic algorithms},
  journal   = {Networks},
  year      = {1995},
  volume    = {26},
  number    = {3},
  pages     = {151--163},
  issn      = {1097-0037},
  abstract  = {This paper describes a new approach to finding solutions to the optimal communication spanning tree problem (OCSTP) using a genetic algorithm. The difficulties posed by this problem are reviewed and a genetic algorithm that consistently finds very good, if not optimal, solutions to it is presented. Finally, a comparison of the genetic algorithm's solutions to those produced by a good heuristic is given that demonstrates the genetic algorithm's ability to find solutions at least equivalent to, if not superior to, those found by the heuristic.},
  doi       = {10.1002/net.3230260305},
  publisher = {Wiley Subscription Services, Inc., A Wiley Company},
}

@Book{Rothlauf2006,
  Title                    = {Representations for Genetic and Evolutionary Algorithms},
  Author                   = {Franz Rothlauf},
  Publisher                = {Springer},
  Year                     = {2006},
  Edition                  = {2},

  ISBN                     = {978-3-540-32444-7}
}

@Book{Shiffman2012,
  title     = {The Nature of Code},
  publisher = {The Nature of Code},
  year      = {2012},
  author    = {Daniel Shiffman},
  edition   = {1},
  month     = dec,
  pages     = {520},
  quality   = {1},
  timestamp = {2013.06.17},
  url       = {http://natureofcode.com/book/},
}

@Book{Sivanandam2010,
  Title                    = {Introduction to Genetic Algorithms},
  Author                   = {S. N. Sivanandam and S. N. Deepa},
  Publisher                = {Springer},
  Year                     = {2010}
}

@Article{FEDR:FEDR19750860506,
  author    = {Vent, W.},
  title     = {Rechenberg, Ingo, Evolutionsstrategie --- Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann-Holzboog-Verlag. Stuttgart 1973. Broschiert},
  journal   = {Feddes Repertorium},
  year      = {1975},
  volume    = {86},
  number    = {5},
  pages     = {337--337},
  issn      = {1522-239X},
  doi       = {10.1002/fedr.19750860506},
  publisher = {Wiley-VCH Verlag},
}

@Article{MathWorld_Weisstein,
  Title                    = {Scalar Function},
  Author                   = {Weisstein, Eric W.},
  Journal                  = {http://mathworld.wolfram.com/-ScalarFunction.html},
  Year                     = {2015},

  Abstract                 = {A function f(x_1,...,x_n) of one or more variables whose range is one-dimensional, as compared to a vector function, whose range is three-dimensional (or, in general, n-dimensional).},
  HowPublished             = {MathWorld},
  Url                      = {http://mathworld.wolfram.com/ScalarFunction.html}
}

@Article{MathWorld_Weisstein2,
  Title                    = {Vector Function},
  Author                   = {Weisstein, Eric W.},
  Journal                  = {http://mathworld.wolfram.com/-VectorFunction.html},
  Year                     = {2015},

  Abstract                 = {A function of one or more variables whose range is three-dimensional (or, in general, n-dimensional), as compared to a scalar function, whose range is one-dimensional. Vector functions are also called vector-valued functions. },
  HowPublished             = {MathWorld},
  Url                      = {http://mathworld.wolfram.com/VectorFunction.html}
}

@Article{Whitley94agenetic,
  Title                    = {A Genetic Algorithm Tutorial},
  Author                   = {Darrell Whitley},
  Journal                  = {Statistics and Computing},
  Year                     = {1994},
  Pages                    = {65--85},
  Volume                   = {4},

  Citeseerurl              = {http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.179}
}

@Article{WikipediaWeaselProgram,
  Title                    = {Weasel program --- {W}ikipedia{,} The Free Encyclopedia},
  Author                   = {Wikipedia},
  Journal                  = {https://en.wikipedia.org/wiki/Weasel_program},
  Year                     = {2015},
  Note                     = {[Online; accessed 04-01-2004]},

  Url                      = {https://en.wikipedia.org/wiki/Weasel_program}
}

@Article{Wikipedi2012,
  Title                    = {Genetic algorithm --- {W}ikipedia{,} The Free Encyclopedia},
  Author                   = {Wikipedia},
  Journal                  = {https://en.wikipedia.org/wiki/Genetic_algorithm},
  Year                     = {2012},
  Note                     = {[Online; accessed 04-01-2004]},

  Url                      = {https://en.wikipedia.org/wiki/Genetic_algorithm}
}

@Article{JSSv068c01,
  Title                    = {A Genetic Algorithm for Selection of Fixed-Size Subsets with Application to Design Problems},
  Author                   = {Mark Wolters},
  Journal                  = {Journal of Statistical Software},
  Year                     = {2015},
  Number                   = {1},
  Pages                    = {1--18},
  Volume                   = {68},

  Abstract                 = {The R function kofnGA conducts a genetic algorithm search for the best subset of k items from a set of n alternatives, given an objective function that measures the quality of a subset. The function fills a gap in the presently available subset selection software, which typically searches over a range of subset sizes, restricts the types of objective functions considered, or does not include freely available code. The new function is demonstrated on two types of problem where a fixed-size subset search is desirable: design of environmental monitoring networks, and D-optimal design of experiments. Additionally, the performance is evaluated on a class of constructed test problems with a novel design that is interesting in its own right.},
  Doi                      = {10.18637/jss.v068.c01},
  ISSN                     = {1548-7660},
  Keywords                 = {discrete optimization; heuristics; evolutionary computation; network design; D-optimal design; optimization test problems},
  Url                      = {http://www.jstatsoft.org/index.php/jss/article/view/v068c01}
}

@Article{Muhlenbein:1993:PMB:1326623.1326626,
  author       = {M\"{u}hlenbein, Heinz and Schlierkamp-Voosen, Dirk},
  title        = {Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization},
  volume       = {1},
  number       = {1},
  pages        = {25--49},
  issn         = {1063-6560},
  abstract     = {In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented that is derived from quantitative genetics. The model is used to predict the behavior of the BGA for simple test functions. Different mutation schemes are compared by computing the expected progress to the solution. The numerical performance of the BGA is demonstrated on a test suite of multimodal functions. The number of function evaluations needed to locate the optimum scales only as n ln(n) where n is the number of parameters. Results up to n = 1000 are reported.},
  acmid        = {1326626},
  date         = {1993-03},
  doi          = {10.1162/evco.1993.1.1.25},
  issue_date   = {Spring 1993},
  journaltitle = {Evol. Comput.},
  location     = {Cambridge, MA, USA},
  numpages     = {25},
  publisher    = {MIT Press},
}

@Book{Koza:1992:GPP:138936,
  title     = {Genetic Programming: On the Programming of Computers by Means of Natural Selection},
  publisher = {MIT Press},
  year      = {1992},
  author    = {Koza, John R.},
  address   = {Cambridge, MA, USA},
  isbn      = {0-262-11170-5},
  timestamp = {2017.07.24},
}

@Book{Koza:1999:GPI:553446,
  title     = {Genetic Programming III: Darwinian Invention \& Problem Solving},
  publisher = {Morgan Kaufmann Publishers Inc.},
  year      = {1999},
  author    = {Koza, John R. and Andre, David and Bennett, Forrest H. and Keane, Martin A.},
  address   = {San Francisco, CA, USA},
  edition   = {1\textsuperscript{st}},
  isbn      = {1558605436},
  owner     = {fwilhelm},
  timestamp = {2017.07.25},
}

@Book{Koza:1994:GPI:183460,
  title     = {Genetic Programming II: Automatic Discovery of Reusable Programs},
  publisher = {MIT Press},
  year      = {1994},
  author    = {Koza, John R.},
  address   = {Cambridge, MA, USA},
  isbn      = {0-262-11189-6},
  owner     = {fwilhelm},
  timestamp = {2017.07.25},
}

@InProceedings{Koza:2008:IGP:1388969.1389057,
  author    = {Koza, John R.},
  title     = {Introduction to Genetic Programming: Tutorial},
  booktitle = {Proceedings of the 10\textsuperscript{th} Annual Conference Companion on Genetic and Evolutionary Computation},
  year      = {2008},
  series    = {GECCO '08},
  pages     = {2299--2338},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1389057},
  doi       = {10.1145/1388969.1389057},
  isbn      = {978-1-60558-131-6},
  keywords  = {genetic programming},
  location  = {Atlanta, GA, USA},
  numpages  = {40},
  owner     = {fwilhelm},
  timestamp = {2017.07.25},
}

@Book{Koza:2003:GPI:945774,
  title     = {Genetic Programming IV: Routine Human-Competitive Machine Intelligence},
  publisher = {Kluwer Academic Publishers},
  year      = {2003},
  author    = {Koza, John R.},
  address   = {Norwell, MA, USA},
  isbn      = {1402074468},
  owner     = {fwilhelm},
  timestamp = {2017.07.25},
}

@Article{Deb99self-adaptivegenetic,
  author    = {Kalyanmoy Deb and Hans-Georg Beyer},
  title     = {Self-Adaptive Genetic Algorithms with Simulated Binary Crossover},
  journal   = {COMPLEX SYSTEMS},
  year      = {1999},
  volume    = {9},
  pages     = {431--454},
  owner     = {fwilhelm},
  timestamp = {2017.07.27},
}

@Misc{GoodPRNGPractice,
  author               = {Jones, David},
  title                = {Good Practice in (Pseudo) Random Number Generation for Bioinformatics Applications},
  month                = may,
  year                 = {2010},
  citeulike-article-id = {12238550},
  citeulike-linkout-0  = {http://www.cs.ucl.ac.uk/staff/d.jones/GoodPracticeRNG.pdf},
  day                  = {7},
  institution          = {University College London},
  keywords             = {rng, simulation},
  owner                = {fwilhelm},
  posted-at            = {2013-04-03 11:49:52},
  timestamp            = {2017.07.28},
  url                  = {http://www.cs.ucl.ac.uk/staff/d.jones/GoodPracticeRNG.pdf},
}

@Book{hughes2014computer,
  title     = {Computer Graphics: Principles and Practice},
  publisher = {Addison-Wesley},
  year      = {2014},
  author    = {Hughes, J.F. and Foley, J.D.},
  series    = {The systems programming series},
  isbn      = {9780321399526},
  lccn      = {2012045569},
  url       = {https://books.google.at/books?id=OVpsAQAAQBAJ},
}

@Article{Deb:2002:FEM:2221359.2221582,
  author     = {Deb, K. and Pratap, A. and Agarwal, S. and Meyarivan, T.},
  title      = {A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II},
  journal    = {Trans. Evol. Comp},
  year       = {2002},
  volume     = {6},
  number     = {2},
  pages      = {182--197},
  month      = apr,
  issn       = {1089-778X},
  acmid      = {2221582},
  address    = {Piscataway, NJ, USA},
  doi        = {10.1109/4235.996017},
  issue_date = {April 2002},
  numpages   = {16},
  publisher  = {IEEE Press},
}

@Book{CoelloCoello2007,
  title     = {Evolutionary Algorithms for Solving Multi-Objective Problems},
  publisher = {Springer},
  year      = {2007},
  author    = {Carlos A. Coello Coello, Gary B. Lamont, David A. Van Veldhuizen},
  series    = {Genetic and Evolutionary Computation},
  address   = {Berlin, Heidelberg},
  edition   = {2\textsuperscript{nd}},
  added-at  = {2009-12-14T10:56:13.000+0100},
  biburl    = {https://www.bibsonomy.org/bibtex/2069f53236f428965116e5b5017442b29/danfunky},
  doi       = {10.1007/978-0-387-36797-2},
  interhash = {9e01931bd73c14005547b22cf888f33a},
  intrahash = {069f53236f428965116e5b5017442b29},
  keywords  = {algorithms, evolutionary multi-objective, optimization},
  owner     = {D047718},
  timestamp = {2009-12-14T10:56:16.000+0100},
  url       = {http://books.google.com/books?id=rXIuAMw3lGAC},
}

@InProceedings{Fortin2013,
  author    = {Fortin, F{\'e}lix-Antoine and Parizeau, Marc},
  title     = {Revisiting the NSGA-II Crowding-distance Computation},
  booktitle = {Proceedings of the 15\textsuperscript{th} Annual Conference on Genetic and Evolutionary Computation},
  year      = {2013},
  series    = {GECCO '13},
  pages     = {623--630},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {2463456},
  doi       = {10.1145/2463372.2463456},
  isbn      = {978-1-4503-1963-8},
  keywords  = {crowding distance, multi-objective evolutionary algorithms, nsga-ii},
  location  = {Amsterdam, The Netherlands},
  numpages  = {8},
}

@Article{Konak2006,
  author    = {Konak, Abdullah and Coit, David W. and Smith, Alice E.},
  title     = {Multi-objective optimization using genetic algorithms: A tutorial.},
  journal   = {Rel. Eng. \& Sys. Safety},
  year      = {2006},
  volume    = {91},
  number    = {9},
  pages     = {992--1007},
  added-at  = {2012-06-20T00:00:00.000+0200},
  biburl    = {https://www.bibsonomy.org/bibtex/28a293983a13e46a6acf0b157c05e85cb/dblp},
  doi       = {10.1016/j.ress.2005.11.018},
  interhash = {3a1e5f8c2a4f2f6cd649841072c9e3c1},
  intrahash = {8a293983a13e46a6acf0b157c05e85cb},
  keywords  = {dblp},
  timestamp = {2012-06-21T11:35:16.000+0200},
  url       = {http://dblp.uni-trier.de/db/journals/ress/ress91.html#KonakCS06},
}

@Article{Osyczka1985,
  author    = {A. Osyczka},
  title     = {Multicriteria optimization for engineering design},
  journal   = {Design Optimization},
  year      = {1985},
  pages     = {193--227},
  owner     = {fwilhelm},
  timestamp = {2017.12.15},
}

@InBook{Deb2001,
  title     = {Scalable test problems for evolutionary multi-objective optimization},
  publisher = {ETH-Zentrum},
  year      = {2001},
  author    = {Kalyanmoy Deb and Lothar Thiele and Marco Laumanns and Eckart Zitzler},
  number    = {112},
  series    = {TIK-Technical Report},
  address   = {ETH-Zentrum Switzerland},
  month     = jul,
  abstract  = {After adequately demonstrating the ability to solve different two-objective optimization
problems, multi-objective evolutionary algorithms (MOEAs) must now show their efficacy in
handling problems having more than two objectives. In this paper, we have suggested three
different approaches for systematically designing test problems for this purpose. The simplicity
of construction, scalability to any number of decision variables and objectives, knowledge of
exact shape and location of the resulting Pareto-optimal front, and introduction of controlled
difficulties in both converging to the true Pareto-optimal front and maintaining a widely
distributed set of solutions are the main features of the suggested test problems. Because
of the above features, they should be found useful in various research activities on MOEAs,
such as testing the performance of a new MOEA, comparing different MOEAs, and better
understanding of the working principles of MOEAs.},
  doi       = {10.3929/ethz-a-004284199},
}

@Book{Bloch2018,
  title     = {Effective Java},
  publisher = {Addison-Wesley Professional},
  year      = {2018},
  author    = {Joshua Bloch},
  edition   = {3\textsuperscript{rd}},
  isbn      = {978-0134685991},
  url       = {https://www.pearson.com/us/higher-education/program/Bloch-Effective-Java-3rd-Edition/PGM1763855.html},
}

@Article{Jain:1985:PAD:4372.4378,
  author     = {Jain, Raj and Chlamtac, Imrich},
  title      = {The P2 Algorithm for Dynamic Calculation of Quantiles and Histograms Without Storing Observations},
  journal    = {Commun. ACM},
  year       = {1985},
  volume     = {28},
  number     = {10},
  pages      = {1076--1085},
  month      = oct,
  issn       = {0001-0782},
  acmid      = {4378},
  address    = {New York, NY, USA},
  doi        = {10.1145/4372.4378},
  issue_date = {Oct. 1985},
  numpages   = {10},
  publisher  = {ACM},
}
@book{baader_nipkow_1998, place={Cambridge}, title={Term Rewriting and All That}, DOI={10.1017/CBO9781139172752}, publisher={Cambridge University Press}, author={Baader, Franz and Nipkow, Tobias}, year={1998}}

@inproceedings{Michalewicz1995ASO,
  title={A Survey of Constraint Handling Techniques in Evolutionary Computation Methods},
  author={Zbigniew Michalewicz},
  booktitle={Evolutionary Programming},
  year={1995}
}

@Book{Datta2014,
  title  = {Evolutionary Constrained Optimization},
  year   = {2014},
  author = {Datta, Rituparna and Deb, Kalyanmoy},
  month  = dec,
  isbn   = {978-81-322-2183-8},
  doi    = {10.1007/978-81-322-2184-5},
}

@Book{Mezura-Montes2009,
  title  = {Constraint-Handling in Evolutionary Optimization},
  year   = {2009},
  author = {Mezura-Montes, Efr{\'{e}}n},
  volume = {198},
  month  = jan,
  isbn   = {978-3-642-00618-0},
  doi    = {10.1007/978-3-642-00619-7},
}

@Article{Coello2002,
  author  = {Coello, Carlos},
  title   = {Coello, A.C.: Theoretical and Numerical Constraint-Handling Techniques Used with Evolutionary Algorithms: A Survey of the State of the Art. Comput. Methods Appl. Mech. Engrg. 191(11-12), 1245-1287},
  journal = {Computer Methods in Applied Mechanics and Engineering},
  year    = {2002},
  volume  = {191},
  pages   = {1245--1287},
  month   = jan,
  doi     = {10.1016/S0045-7825(01)00323-1},
}

@InProceedings{ryan1998grammatical,
  author            = {Conor Ryan and J. J. Collins and Michael O'Neill},
  title             = {Grammatical Evolution: Evolving Programs for an Arbitrary Language},
  booktitle         = {Proceedings of the First European Workshop on Genetic Programming},
  year              = {1998},
  editor            = {Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty},
  volume            = {1391},
  series            = {LNCS},
  pages             = {83--96},
  address           = {Paris},
  month             = {14-15 } # apr,
  publisher         = {Springer-Verlag},
  abstract          = {We describe a Genetic Algorithm that can evolve complete programs. Using a variable length linear genome to govern how a Backus Naur Form grammar definition is mapped to a program, expressions and programs of arbitrary complexity may be evolved. Other automatic programming methods are described, before our system, Grammatical Evolution, is applied to a symbolic regression problem.},
  affiliation       = {University of Limerick Dept. Of Computer Science and Information Systems Ireland Ireland},
  doi               = {10.1007/BFb0055930},
  isbn              = {3-540-64360-5},
  keywords          = {genetic algorithms, genetic programming, grammatical evolution},
  notes             = {EuroGP'98},
  publisher_address = {Berlin},
  size              = {14 pages},
  url               = {http://www.lania.mx/~ccoello/eurogp98.ps.gz},
}

@InProceedings{Lourenco2016,
  author    = {Louren{\c{c}}o, Nuno and Pereira, Francisco B. and Costa, Ernesto},
  title     = {SGE: A Structured Representation for Grammatical Evolution},
  booktitle = {Artificial Evolution},
  year      = {2016},
  editor    = {Bonnevay, St{\'e}phane and Legrand, Pierrick and Monmarch{\'e}, Nicolas and Lutton, Evelyne and Schoenauer, Marc},
  pages     = {136--148},
  address   = {Cham},
  publisher = {Springer International Publishing},
  abstract  = {This paper introduces Structured Grammatical Evolution, a new genotypic representation for Grammatical Evolution, where each gene is explicitly linked to a non-terminal of the grammar being used. This one-to-one correspondence ensures that the modification of a gene does not affect the derivation options of other non-terminals, thereby increasing locality. The performance of the new representation is accessed on a set of benchmark problems. The results obtained confirm the effectiveness of the proposed approach, as it is able to outperform standard grammatical evolution on all selected optimization problems.},
  isbn      = {978-3-319-31471-6},
  owner     = {fwilhelm},
  timestamp = {2022.04.03},
}

@InProceedings{Assuncao2017,
  author    = {Assun\c{c}\~{a}o, Filipe and Louren\c{c}o, Nuno and Machado, Penousal and Ribeiro, Bernardete},
  title     = {Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
  year      = {2017},
  series    = {GECCO '17},
  pages     = {393--400},
  address   = {New York, NY, USA},
  publisher = {Association for Computing Machinery},
  abstract  = {Current grammar-based NeuroEvolution approaches have several shortcomings. On the one hand, they do not allow the generation of Artificial Neural Networks (ANNs) composed of more than one hidden-layer. On the other, there is no way to evolve networks with more than one output neuron. To properly evolve ANNs with more than one hidden-layer and multiple output nodes there is the need to know the number of neurons available in previous layers. In this paper we introduce Dynamic Structured Grammatical Evolution (DSGE): a new genotypic representation that overcomes the aforementioned limitations. By enabling the creation of dynamic rules that specify the connection possibilities of each neuron, the methodology enables the evolution of multi-layered ANNs with more than one output neuron. Results in different classification problems show that DSGE evolves effective single and multi-layered ANNs, with a varying number of output neurons.},
  doi       = {10.1145/3071178.3071286},
  isbn      = {9781450349208},
  keywords  = {NeuroEvolution, Artificial Neural Networks, Grammar-based Genetic Programming, Classification},
  location  = {Berlin, Germany},
  numpages  = {8},
  owner     = {fwilhelm},
  timestamp = {2022.04.03},
}

@Article{Bartoli2019,
  author    = {Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Giovanni Squillero},
  title     = {Multi-level diversity promotion strategies for Grammar-guided Genetic Programming},
  journal   = {Applied Soft Computing},
  year      = {2019},
  volume    = {83},
  pages     = {105599},
  issn      = {1568-4946},
  abstract  = {Grammar-guided Genetic Programming (G3P) is a family of Evolutionary Algorithms that can evolve programs in any language described by a context-free grammar. The most widespread members of this family are based on an indirect representation: a sequence of bits or integers (the genotype) is transformed into a string of the language (the phenotype) by means of a mapping function, and eventually into a fitness value. Unfortunately, the flexibility brought by this mapping is also likely to introduce non-locality phenomena, reduce diversity, and hamper the effectiveness of the algorithm. In this paper, we experimentally characterize how population diversity, measured at different levels, varies for four popular G3P approaches. We then propose two strategies for promoting diversity which are general, independent both from the specific problem being tackled and from the other components of the Evolutionary Algorithm, such as genotype–phenotype mapping, selection criteria, and genetic operators. We experimentally demonstrate their efficacy in a wide range of conditions and from different points of view. The results also confirm the preponderant importance of the phenotype-level analyzes in diversity promotion.},
  doi       = {10.1016/j.asoc.2019.105599},
  keywords  = {Representation, Grammatical evolution, CFGGP, SGE, WHGE},
  owner     = {fwilhelm},
  timestamp = {2022.04.03},
  url       = {https://www.sciencedirect.com/science/article/pii/S1568494619303795},
}

@Book{ONeil2003,
  title     = {Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language},
  publisher = {Springer US},
  year      = {2003},
  author    = {O'Neil, Michael and Ryan, Conor},
  address   = {Boston, MA},
  isbn      = {978-1-4615-0447-4},
  abstract  = {This book is about Grammatical Evolution (GE), an approach to Genetic Programming that allows the generation of computer programs in an arbitrary language. GE exploits a rich modularity in its design that results in a highly flexible and easy to use system. GE owes this design and, consequently, its most powerful features to inspiration from biological systems. There are two main themes to the book; the first being the adoption of phenomena from molecular biology in a simple and useful manner, and the second being the use of grammars to specify legal structures in a search.},
  booktitle = {Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language},
  doi       = {10.1007/978-1-4615-0447-4},
  timestamp = {2022.04.03},
}

@InProceedings{backus1959syntax,
  author       = {Backus, J},
  title        = {The syntax and semantics of the proposed international algorithmic language},
  year         = {1959},
  series       = {Proceedings of the International Conference on Information Processing–IFIP Congress},
  pages        = {125--132},
  month        = jun,
  organization = {ACM-GAMM},
  abstract     = {This paper gives a tutorial summary of the syntax and interpretation
rules of the proposed international algebraic language put forward by
the Zurich ACM-GAMM Conference, followed by a formal, complete
presentation of the same information. Notations are presented for
numbers, numerical variables, Boolean variables, relations, n-dimen-
sional arrays, functi ons, operator s and algebraic expre s sions. Means
are provided in the language for specifying assignment of values to.
variables, conditional execution of statements, iterative proce<i;ures,
formation of compound statements from sequences of statements,
definition of new statements for arbitrary procedures, reuse and
alteration of program segments.
The proposed language is intended to provide convenient and concise
means for expressing virtually all procedures of numericaL compu-
tation while employing relatively few syntactical rules and statement
types.},
}

@InCollection{Greenlaw1998,
  author    = {Raymond Greenlaw},
  title     = {Grammars},
  booktitle = {Fundamentals of the Theory of Computation: Principles and Practice},
  publisher = {Morgan Kaufmann},
  year      = {1998},
  editor    = {Raymond Greenlaw},
  pages     = {195--220},
  address   = {Oxford},
  isbn      = {978-1-55860-547-3},
  doi       = {10.1016/B978-1-55860-547-3.50011-X},
  url       = {https://www.sciencedirect.com/science/article/pii/B978155860547350011X},
}

@InBook{Ryan2018,
  pages     = {1--21},
  title     = {Introduction to 20 Years of Grammatical Evolution},
  publisher = {Springer International Publishing},
  year      = {2018},
  author    = {Ryan, Conor and O'Neill, Michael and Collins, JJ},
  editor    = {Ryan, Conor and O'Neill, Michael and Collins, JJ},
  address   = {Cham},
  isbn      = {978-3-319-78717-6},
  abstract  = {Grammatical Evolution (GE) is a Evolutionary Algorithm (EA) that takes inspiration from the biological evolutionary process to search for solutions to problems. This chapter gives a brief introduction to EAs, paying particular attention to those involved in automatic program generation. We then describe grammars, the core building blocks of programs, before detailing how GE's usage of them is one of the key differentiators between it and other EAs.},
  booktitle = {Handbook of Grammatical Evolution},
  doi       = {10.1007/978-3-319-78717-6_1},
  owner     = {fwilhelm},
  timestamp = {2022.04.16},
}

@InBook{Kruse2022,
  pages     = {255--285},
  title     = {Elements of Evolutionary Algorithms},
  publisher = {Springer International Publishing},
  year      = {2022},
  author    = {Kruse, Rudolf and Mostaghim, Sanaz and Borgelt, Christian and Braune, Christian and Steinbrecher, Matthias},
  address   = {Cham},
  isbn      = {978-3-030-42227-1},
  abstract  = {Evolutionary algorithms are not fixed procedures, but contain several elements that must be adapted to the optimization problem to be solved. In particular, the encoding of the candidate solution needs to be chosen with care. Although there is no generally valid rule or recipe, we discuss some important properties a good encoding should have. Then we turn to the fitness function and review the most common selection techniques as well as how certain undesired effects can be avoided by adapting the fitness function or the selection method. The final section is devoted to genetic operators, which serve as tools to explore the search space, and covers sexual and asexual recombination and other variation techniques.},
  booktitle = {Computational Intelligence: A Methodological Introduction},
  doi       = {10.1007/978-3-030-42227-1_12},
}

@misc{ enwiki:1244797749,
  author = "{Wikipedia contributors}",
  title = "Metaheuristic --- {Wikipedia}{,} The Free Encyclopedia",
  year = "2024",
  url = "https://en.wikipedia.org/w/index.php?title=Metaheuristic&oldid=1244797749",
  note = "[Online; accessed 11-September-2024]"
}

@misc{ enwiki:1187882212,
  author = "{Wikipedia contributors}",
  title = "Optimization problem --- {Wikipedia}{,} The Free Encyclopedia",
  year = "2023",
  url = "https://en.wikipedia.org/w/index.php?title=Optimization_problem&oldid=1187882212",
  note = "[Online; accessed 11-September-2024]"
}

@Comment{jabref-meta: databaseType:bibtex;}
